Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Tongue"
Warning message:
package ‘Seurat’ was built under R version 3.4.3
library(here)
here() starts at /Users/olgabot/code/tabula-muris
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
package ‘dplyr’ was built under R version 3.4.2
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
tiss = load_tissue_facs(tissue_of_interest)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data matrix"
|
| | 0%
|
|=================================================================================| 100%
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 10
The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.7
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=30)
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 137-174 (Tongue_facs.Rmd)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7
368 253 196 182 161 121 108 27
tiss1=BuildClusterTree(tiss)
[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
Which markers identify a specific cluster?
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster7.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~01m 24s
|++ | 2 % ~01m 22s
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|++++++++++++++ | 28% ~60s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
print(x = head(x = cluster6.markers, n = 30))
You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~01m 21s
|++ | 2 % ~01m 18s
|++ | 3 % ~01m 17s
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|++++ | 6 % ~01m 15s
|++++ | 7 % ~01m 15s
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|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 07s
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|+++++++++++++ | 26% ~60s
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|++++++++++++++++ | 31% ~55s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 19s
| | 0 % ~calculating
|+ | 1 % ~01m 29s
|++ | 2 % ~01m 29s
|++ | 3 % ~01m 27s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 39s
| | 0 % ~calculating
|+ | 1 % ~02m 28s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 31s
| | 0 % ~calculating
|+ | 1 % ~02m 04s
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|+++++++++++++++++++++++++++++++++++++++ | 77% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~25s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~23s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 05s
| | 0 % ~calculating
|+ | 1 % ~01m 43s
|++ | 2 % ~01m 41s
|++ | 3 % ~01m 39s
|+++ | 4 % ~01m 37s
|+++ | 5 % ~01m 36s
|++++ | 6 % ~01m 35s
|++++ | 7 % ~01m 34s
|+++++ | 8 % ~01m 33s
|+++++ | 9 % ~01m 32s
|++++++ | 10% ~01m 30s
|++++++ | 11% ~01m 30s
|+++++++ | 12% ~01m 30s
|+++++++ | 13% ~01m 28s
|++++++++ | 14% ~01m 27s
|++++++++ | 15% ~01m 26s
|+++++++++ | 16% ~01m 25s
|+++++++++ | 17% ~01m 25s
|++++++++++ | 18% ~01m 23s
|++++++++++ | 19% ~01m 22s
|+++++++++++ | 20% ~01m 21s
|+++++++++++ | 21% ~01m 20s
|++++++++++++ | 22% ~01m 19s
|++++++++++++ | 23% ~01m 18s
|+++++++++++++ | 24% ~01m 17s
|+++++++++++++ | 26% ~01m 16s
|++++++++++++++ | 27% ~01m 15s
|++++++++++++++ | 28% ~01m 14s
|+++++++++++++++ | 29% ~01m 13s
|+++++++++++++++ | 30% ~01m 12s
|++++++++++++++++ | 31% ~01m 11s
|++++++++++++++++ | 32% ~01m 10s
|+++++++++++++++++ | 33% ~01m 09s
|+++++++++++++++++ | 34% ~01m 08s
|++++++++++++++++++ | 35% ~01m 08s
|++++++++++++++++++ | 36% ~01m 07s
|+++++++++++++++++++ | 37% ~01m 05s
|+++++++++++++++++++ | 38% ~01m 04s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 01s
|+++++++++++++++++++++ | 42% ~01m 00s
|++++++++++++++++++++++ | 43% ~60s
|++++++++++++++++++++++ | 44% ~59s
|+++++++++++++++++++++++ | 45% ~57s
|+++++++++++++++++++++++ | 46% ~56s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
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|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 56% ~46s
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|++++++++++++++++++++++++++++++ | 58% ~44s
|++++++++++++++++++++++++++++++ | 59% ~43s
|+++++++++++++++++++++++++++++++ | 60% ~41s
|+++++++++++++++++++++++++++++++ | 61% ~40s
|++++++++++++++++++++++++++++++++ | 62% ~39s
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|+++++++++++++++++++++++++++++++++ | 64% ~37s
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|++++++++++++++++++++++++++++++++++ | 67% ~34s
|+++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|++++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|+++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~27s
|++++++++++++++++++++++++++++++++++++++ | 74% ~26s
|++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
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|++++++++++++++++++++++++++++++++++++++++++ | 84% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
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|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 43s
| | 0 % ~calculating
|+ | 1 % ~03m 16s
|+ | 2 % ~03m 21s
|++ | 3 % ~03m 19s
|++ | 4 % ~03m 16s
|+++ | 5 % ~03m 15s
|+++ | 6 % ~03m 13s
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|+++++ | 9 % ~03m 11s
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|++++++++ | 15% ~03m 03s
|++++++++ | 16% ~03m 01s
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|+++++++++++ | 22% ~02m 50s
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|++++++++++++++ | 27% ~02m 39s
|++++++++++++++ | 28% ~02m 36s
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|++++++++++++++++ | 31% ~02m 29s
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|+++++++++++++++++ | 33% ~02m 24s
|+++++++++++++++++ | 34% ~02m 22s
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|++++++++++++++++++++ | 39% ~02m 12s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 30s
| | 0 % ~calculating
|+ | 1 % ~01m 55s
|++ | 2 % ~01m 54s
|++ | 3 % ~01m 53s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 01s
| | 0 % ~calculating
|+ | 1 % ~03m 12s
|++ | 2 % ~03m 07s
|++ | 3 % ~03m 06s
|+++ | 4 % ~03m 02s
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|++++++++ | 14% ~02m 42s
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|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~22s
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~14s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 15s
Display the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
0: basal cell of epidermis (CL:0002187) 1: basal cell of epidermis (CL:0002187) 2: proliferating basal cell of epidermis (CL:0002187) 3: differentiated keratinocytes (CL:0000312) 4: differentiating basal cell of epidermis (CL:0002187) 5: suprabasal differentiating keratinocytes (CL:0000312) 6: basal cell of epidermis (CL:0002187) 7: filiform differentiated keratinocytes (CL:0000312)
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free_annotation <- c(
"basal cells",
"basal cells",
"proliferating",
"differentiated keratinocyte",
"differentiating basal cells",
"suprabasal differentiating keratinocytes",
"basal cells",
"filiform differentiated keratinocytes"
)
cell_ontology_class <- c(
"basal cell of epidermis",
"basal cell of epidermis",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte"
)
validate_cell_ontology(cell_ontology_class)
cell_ontology_id = convert_to_cell_ontology_id(cell_ontology_class)
tiss@meta.data[,'free_annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'free_annotation'] <- as.character(tiss@meta.data$free_annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
B002777 B002780 D041894 D041904 D041911
0 61 58 90 79 80
1 46 12 67 56 72
2 85 24 28 45 14
3 40 19 38 46 39
4 20 8 44 48 41
5 10 5 35 34 37
6 1 7 29 14 57
7 3 3 7 11 3
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
[1] "/Users/olgabot/code/tabula-muris/00_data_ingest/04_tissue_robj_generated/facsTongue_seurat_tiss.Robj"
save(tiss, file=filename)
# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)
filename = here('00_data_ingest', '03_tissue_annotation_csv',
paste0(tissue_of_interest, "_facs_annotation.csv"))
write.csv(FetchData(tiss, c('plate.barcode','cell_ontology_class','cell_ontology_id', 'free_annotation', 'tSNE_1', 'tSNE_2')), file=filename)
sessionInfo()
R version 3.4.1 (2017-06-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2 ontologyIndex_2.4 dplyr_0.7.4 useful_1.2.3
[5] here_0.1 Seurat_2.2.1 cowplot_0.8.0 ggplot2_2.2.1
[9] Matrix_1.2-10
loaded via a namespace (and not attached):
[1] diffusionMap_1.1-0 Rtsne_0.13 VGAM_1.0-4 colorspace_1.3-2
[5] class_7.3-14 modeltools_0.2-21 ggridges_0.4.1 rprojroot_1.2
[9] mclust_5.3 htmlTable_1.9 base64enc_0.1-3 proxy_0.4-17
[13] DRR_0.0.2 flexmix_2.3-14 prodlim_1.6.1 mvtnorm_1.0-6
[17] lubridate_1.7.1 ranger_0.8.0 codetools_0.2-15 splines_3.4.1
[21] R.methodsS3_1.7.1 mnormt_1.5-5 robustbase_0.92-7 knitr_1.17
[25] RcppRoll_0.2.2 tclust_1.3-1 jsonlite_1.5 Formula_1.2-2
[29] caret_6.0-77 ica_1.0-1 ddalpha_1.3.1 cluster_2.0.6
[33] kernlab_0.9-25 R.oo_1.21.0 sfsmisc_1.1-1 compiler_3.4.1
[37] backports_1.1.1 assertthat_0.2.0 lazyeval_0.2.0 lars_1.2
[41] acepack_1.4.1 htmltools_0.3.6 tools_3.4.1 igraph_1.1.2
[45] gtable_0.2.0 glue_1.1.1 reshape2_1.4.2 Rcpp_0.12.13
[49] trimcluster_0.1-2 gdata_2.18.0 ape_4.1 nlme_3.1-131
[53] iterators_1.0.8 fpc_2.1-10 timeDate_3012.100 gower_0.1.2
[57] stringr_1.2.0 irlba_2.2.1 gtools_3.5.0 DEoptimR_1.0-8
[61] MASS_7.3-47 scales_0.5.0 ipred_0.9-6 parallel_3.4.1
[65] RColorBrewer_1.1-2 yaml_2.1.14 pbapply_1.3-3 gridExtra_2.3
[69] rpart_4.1-11 segmented_0.5-2.2 latticeExtra_0.6-28 stringi_1.1.5
[73] foreach_1.4.3 checkmate_1.8.4 caTools_1.17.1 lava_1.5.1
[77] SDMTools_1.1-221 rlang_0.1.4 pkgconfig_2.0.1 dtw_1.18-1
[81] prabclus_2.2-6 bitops_1.0-6 evaluate_0.10.1 lattice_0.20-35
[85] ROCR_1.0-7 purrr_0.2.4 bindr_0.1 labeling_0.3
[89] recipes_0.1.0 htmlwidgets_0.9 tidyselect_0.2.0 CVST_0.2-1
[93] plyr_1.8.4 magrittr_1.5 R6_2.2.2 gplots_3.0.1
[97] Hmisc_4.0-3 dimRed_0.1.0 foreign_0.8-69 withr_2.0.0
[101] sn_1.5-0 mixtools_1.1.0 survival_2.41-3 scatterplot3d_0.3-40
[105] nnet_7.3-12 tsne_0.1-3 tibble_1.3.4 KernSmooth_2.23-15
[109] rmarkdown_1.6 grid_3.4.1 data.table_1.10.4 FNN_1.1
[113] ModelMetrics_1.1.0 metap_0.8 digest_0.6.12 diptest_0.75-7
[117] tidyr_0.7.2 numDeriv_2016.8-1 R.utils_2.5.0 stats4_3.4.1
[121] munsell_0.4.3